Identification

IDNO

BTN_2010_MICS_v01_M

Title

Multiple Indicator Cluster Survey 2010

Countries

Name

Code

Bhutan

BTN

Study notes

The National Statistics Bureau conducted the Bhutan Multiple Indicator Survey between March and August, 2010. The survey’s main objective is to provide up-to-date information on the situation of children and women in Bhutan. The survey is also aimed at furnishing data required for monitoring progress towards the MDGs, the goals of A World Fit for Children and other international goals. It is hoped that the findings will serve as a basis for equity-based programming, as well as contribute towards the improvement of data and monitoring systems in Bhutan. It will also help to strengthen technical expertise in the design, implementation, and data analysis of similar surveys in future.

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Producers and sponsors

Authoring entity

Name

Affiliation

National Statistics Bureau

Royal Government of Bhutan

United Nations Children’s Fund

United Nations Population Fund

Funding agencies

Name

Abbreviation

Role

United Nations Children’s Fund

UNICEF

Financial and technical support

United Nations Population Fund

UNFPA

Financial and technical support

Other acknowledgement(s)

Name

Ministry of Health

Ministry of Education

Gross National Happiness Commission

National Commission for Women and Children

Sampling

Sampling procedure

The primary objective of the sample design for the Bhutan Multiple Indicator Survey was to produce statistically reliable estimates of most indicators at the national level, for urban and rural areas, and for the 20 Dzongkhags of the country. Urban and rural areas in each of the 20 Dzongkhags were defined as the sampling strata.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

The target sample size for the BMIS was calculated as 15,400 households. For the calculation of the sample size, the key indicator used was the stunting among children aged 0-4 years.

The resulting number of households from this exercise was 800 households, which is the sample size needed in each Dzongkhag except Gasa (200 households) after taking account of the finite population correction factor - thus yielding about 15,400 in total. Gasa was a special case, in that it has a very small population, and widely dispersed. It was felt that 200 households was the maximum sample size that could realistically be achieved in that Dzongkhag.The average number of households selected per cluster for the BMIS was determined as 20 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 40 sample clusters would need to be selected in each Dzongkhag except Gasa (10).

Equal allocation of the total sample size to the 20 Dzongkhags was used except Gasa. Therefore, 40 clusters were allocated to each Dzongkhag except Gasa (10), with the final sample size calculated at 15,400 households: 40 clusters * 19 Dzongkhags * 20 sample households per cluster and 10 clusters*1 Dzongkhag *20 sample households per cluster in Gasa. In each Dzongkhag, the clusters (primary sampling units) were distributed to urban and rural domains, proportional to the size of urban and rural populations in that Dzongkhag. The table below shows the allocation of clusters to the sampling strata.

Of the 15,400 households selected for the sample, 14,917 were occupied. Of which, 14,676 households were successfully interviewed for a household response rate of 98.4 percent. Within those interviewed households, 16,823 of the eligible women (aged 15-49) were identified. Of them 14,018 were successfully interviewed, yielding a response rate of 83.3 percent. The household interviews identified 6,457 children under-five. The questionnaires were completed for 6,297 of them with a response rate of 97.5 percent.

Weighting

Sample weights were calculated and these were used in the subsequent analyses of the survey data.

The major component of the weight is the reciprocal of the sampling fraction employed in selecting the number of sample households in that particular sampling stratum and PSU. The sampling fraction for the sample PSU in the stratum is the product of probabilities of selection at every stage in each sampling stratum.

A second component in the calculation of sample weights takes into account the level of non­ response for the household and individual interviews. The adjustment for household non­ response is equal to the inverse value of: RRh = Number of interviewed households in stratum h/ Number of occupied households listed in stratum h

After the completion of fieldwork, response rates were calculated for each sampling stratum. These were used to adjust the sample weights calculated for each cluster. The non-response adjustment factors for women's and under-5's questionnaires are applied to the adjusted household weights. Numbers of eligible women and under-5 children were obtained from the roster of household members in the Household Questionnaire for households where interviews were completed.

The design weights for the households were calculated by multiplying the above factors for each enumeration area. These weights were then standardized (or normalized), one purpose of which is to make the weighted sum of the interviewed sample units equal the total sample size at the national level. Normalization is performed by dividing the aforementioned design weights by the average design weight at the national level. The average design weight is calculated as the sum of the design weights divided by the unweighted total). A similar standardization procedure was followed in obtaining standardized weights for the women’s and under-five’s questionnaires. Adjusted (normalized) weights varied between 0.84 and 23.4 in the 770 sample enumeration areas (clusters).

Sample weights were appended to all data sets and analyses were performed by weighting each household, woman or under-five with these sample weights.

Data Collection

Dates of collection

Start

End

2010-04

2010-08

Mode of data collection

Face-to-face [f2f]

Data collection supervision

There is one supervisor for each of the 42 survey teams in the field.

Questionnaires

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes Household Listing Form, Education, Water and Sanitation, Household Characteristics, Child Labor, Disability, and Hand Washing.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. The questionnaire for children under 5 years of age was administered to mothers or caregivers of all children under 5 years of age living in the households.

Data Processing

Data editing

Data was entered using the CSPro software in 25 micro-computers and the entry was carried out by 25 operators and three supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS4 programme and adapted to the Bhutan questionnaire were used throughout. Data entry began a month after the start of data collection and was completed in September 2010. Data was analysed using the Predictive Analytics Software (PASW), the version 18 of SPSS software, and the model syntax and tabulation plans developed by UNICEF were used for this purpose.

Data Appraisal

metadata.study_desc.method.analysis_info.sampling_error_estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each of the selected indicators:
• Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors.
• Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error.
• Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design.
• Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r – 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest, for the national level, for the regions, and for urban and rural areas. Eight of the selected indicators are based household members, eighteen are based on women, and twelve are based on children under-five. All indicators presented here are in the form of proportions.

Other forms of data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

- Age distribution of the household population
- Age distribution of eligible and interviewed women
- Age distribution of children under 5 in household and children under 5 questionnaires
- Women’s completion rates by socio-economic characteristics of households
- Completion rates for under-five questionnaires by socio-economic characteristics of households
- Completeness of reporting
- Completeness of information for anthropometric indicators
- Heaping in anthropometric measurements
- Observation of places for hand washing
- Observation of women's health cards
- Observation of children under 5 birth certificates
- Presence of mother in the household and the person interviewed for the under-5 questionnaire
- School attendance by single age
- Sex ratio at birth among children ever born and living

The results of each of these data quality tables are shown in appendix D in document "Bhutan Multiple Indicator Cluster Survey 2010 - Final Report" pp.240-251.

Data access

Access authorities

Name

Affiliation

Email

URI

Childinfo

UNICEF

mics@unicef.org

http://www.childinfo.org/mics4_surveys.html

metadata.study_desc.data_access.dataset_use.conf_dec

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- the Identification of the Primary Investigator
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